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Record W4213006939 · doi:10.1186/s40413-016-0108-1

World Allergy Organization-McMaster University Guidelines for Allergic Disease Prevention (GLAD-P): Vitamin D

2016· article· en· W4213006939 on OpenAlexaff
Juan José Yepes-Núñez, Alessandro Fiocchi, Ruby Pawankar, Carlos A. Cuello‐García, Yuan Zhang, Gian Paolo Morgano, Kangmo Ahn, Suleiman Al‐Hammadi, Arnav Agarwal, Shreyas Gandhi, Kirsten Beyer, Wesley Burks, Giorgio Walter Canonica, Motohiro Ebisawa, Rose Kamenwa, Bee Wah Lee, Haiqi Li, Susan L. Prescott, John J. Riva, Lanny J. Rosenwasser, Hugh A. Sampson, Michael Spigler, Luigi Terracciano, Andrea Vereda, Susan Waserman, Holger J. Schünemann, Jan Brożek

Bibliographic record

VenueWorld Allergy Organization Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsUniversity of TorontoMcMaster UniversityHealth Sciences CentreMcMaster University Medical Centre
Fundersnot available
KeywordsMedicineGuidelineBreastfeedingVitamin D and neurologyRandomized controlled trialAllergyPediatricsAsthmaDiseaseSystematic reviewIntensive care medicineImmunologyMEDLINEInternal medicinePathology

Abstract

fetched live from OpenAlex

Background The prevalence of allergic diseases is approximately 10 % in infants whose parents and siblings do not have allergic diseases and 20 – 30 % in those with an allergic first-degree relative. Vitamin D is involved in the regulation of the immune system and it may play a role in the development, severity and course of asthma and other allergic diseases. Objective The World Allergy Organization (WAO) convened a guideline panel to develop evidence-based recommendations addressing the use of vitamin D in primary prevention of allergic diseases. Methods Our WAO guideline panel identified the most relevant clinical questions and performed a systematic review of randomized controlled trials and non-randomized studies (NRS), specifically cohort and case-control studies, of vitamin D supplementation for the prevention of allergic diseases. We also reviewed the evidence about values and preferences, and resource requirements (up to January 2015, with an update on January 30, 2016). We followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to develop recommendations. Results Having reviewed the currently available evidence, the WAO guideline panel found no support for the hypothesis that vitamin D supplementation reduces the risk of developing allergic diseases in children. The WAO guideline panel suggest not using vitamin D in pregnant women, breastfeeding mothers, or healthy term infants as a means of preventing the development of allergic diseases. This recommendation does not apply to those mothers and infants who have other indications for prophylactic or therapeutic use of vitamin D. The panel's recommendations are conditional and supported by very low certainty evidence. Conclusions WAO recommendations about vitamin D supplementation for the prevention of allergic diseases support parents, clinicians and other health care professionals in their decisions whether or not to use vitamin D in preventing allergic diseases in healthy, term infants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0050.003
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0210.013

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.302
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations50
Published2016
Admission routes1
Has abstractyes

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